Artificial Intelligence Bo Yuan, Ph.D. Professor Shanghai Jiaotong University

Overview of Machine Intelligence Knowledge-based rules (expert system, automata, …) Symbolic representation in logics (Deep Blue) Kernel-based heuristics (MDA, PCA, SVM, …) Nonlinear connection for more representation (Neural Network) Inference (Bayesian, Markovian, …) To sparsely sample for convergence (GM) Interactive and stochastic computing (uncertainty, heterogeneity) To possibly overcome the limit of Turin Machine

Interactions The Framework to Study a System Top-Down Bottom-Up

How much can we represent and model a complex and evolving network ?

Low Complexity Solutions for High Complexity Problems Convexity Stability (Metastability) Sampling Ergodicity Convergence Regularization Software and Hardware

Interactions The Framework to Study a System Top-Down Bottom-Up

How much can we represent and model a complex and evolving network ?

Mathematical Foundation Continuous Stochastic Data Representation Mathematical Foundation Mathematical Representation Typical Algorithm AI-Related Question Graph Graph Theory and Variable Reduction Optimization Liner Programming Network Modularity and Organization Logic Algebraic Logic Random Boolean Network, Automata Network Structure and Attractors Circuit Complex Number and Control Theory Linearization Stability and control Network Stability and Control Reasoning Game Theory Evolutionary Game Nash Equilibrium Markov Games Inference Bayes Theorem Believe Propagation Model Searching Causality Inference Discrete Stochastic Markov-based Updating Convergence Meta-stability Evolution and Dynamics Continuous Stochastic Stochastic Differentials Brownian integrals Fokker-Planck Network Dynamics and Control

Review of Lecture One Overview of AI Course Content Knowledge-based rules in logics (expert system, automata, …) : Symbolism in logics Kernel-based heuristics (neural network, SVM, …) : Connection for nonlinearity Learning and inference (Bayesian, Markovian, …) : To sparsely sample for convergence Interactive and stochastic computing (Uncertainty, heterogeneity) : To overcome the limit of Turin Machine Course Content Focus mainly on learning and inference Discuss current problems and research efforts Perception and behavior (vision, robotic, NLP, bionics …) not included Exam Papers (Nature, Science, Nature Review, Modern Review of Physics, PNAS, TICS) Course materials

Outline Knowledge Representation Searching and Logics Perceiving and Acting Learning Uncertainty and Inference